Revisiting physicians' financial incentives in Quebec: a panel system approach
Bibliographic record
Abstract
Do Primary Care Physicians (PCPs) react strategically to financial incentives and if so how? To address this question, we follow a quasi-natural experiment in Quebec, using a panel system technique. In so doing, we both correct for underestimation biases in earlier time series findings and generate new results on the issue of complementarity/substitution between consultations with varying levels of technicality. Under both techniques, we show that PCPs are sensitive to the enforcement and subsequent temporary removals of expenditure caps and more generally, to changes in consultations' relative prices over time. These results support the existence of a discretionary power over the choice of consultation, PCPs increasing strategically the number of the more technical (and therefore more lucrative) consultations when pressed to defend their income. This finding for primary care parallels the now well-established DRG creep in hospitals. The panel system approach offers a better account of the complexity surrounding PCPs' decision-making process. In particular, it successfully addresses issues of physician heterogeneity, jointness between consultations and temporal breaks and generates robust estimates of PCPs volume and quality reactions to regulatory changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".